{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"6\" color=\"black\"><b>Bikeshare数据集上的数据探索</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\" color=\"red\" ><b>导入必要的工具包</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                               #导入结构化数据分析工具集\n",
    "import numpy as np                                #导入数值计算工具集\n",
    "\n",
    "#图表处理工具集\n",
    "import seaborn as sn                              #导入图表统计数据集\n",
    "import matplotlib.pyplot as plt                   #导入数据可视化工具集\n",
    "%matplotlib inline                                 \n",
    "#可以直接在你的python 控制台里面生成图像\n",
    "\n",
    "#设置参数\n",
    "params = {'legend.fontsize':'x-large',\n",
    "         'figure.figsize':(30,10),\n",
    "         'axes.labelsize':'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')                          #设置图表的自定义风格\n",
    "sn.set_context('talk')                             #设置绘图文本参数\n",
    "\n",
    "plt.rcParams.update(params)                        #手工修改了配置文件，希望重新从配置文件载入最新的配置\n",
    "pd.options.display.max_colwidth = 600              #设置最大显示列宽\n",
    "\n",
    "#将数据帧显示为表\n",
    "from IPython.display import display, HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\" color=\"red\"><b>读入数据</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "#print(\"train:\" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">没有缺少数据</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\" color=\"red\"><b>数据探索</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()          #对数据值型特征，用常用统计量观察其分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\" color=\"red\"><b>1.离散特征的分布</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "0      1\n",
      "1      1\n",
      "2      1\n",
      "3      1\n",
      "4      1\n",
      "5      1\n",
      "6      1\n",
      "7      1\n",
      "8      1\n",
      "9      1\n",
      "10     1\n",
      "11     1\n",
      "12     1\n",
      "13     1\n",
      "14     1\n",
      "15     1\n",
      "16     1\n",
      "17     1\n",
      "18     1\n",
      "19     1\n",
      "20     1\n",
      "21     1\n",
      "22     1\n",
      "23     1\n",
      "24     1\n",
      "25     1\n",
      "26     1\n",
      "27     1\n",
      "28     1\n",
      "29     1\n",
      "      ..\n",
      "701    4\n",
      "702    4\n",
      "703    4\n",
      "704    4\n",
      "705    4\n",
      "706    4\n",
      "707    4\n",
      "708    4\n",
      "709    4\n",
      "710    4\n",
      "711    4\n",
      "712    4\n",
      "713    4\n",
      "714    4\n",
      "715    4\n",
      "716    4\n",
      "717    4\n",
      "718    4\n",
      "719    4\n",
      "720    1\n",
      "721    1\n",
      "722    1\n",
      "723    1\n",
      "724    1\n",
      "725    1\n",
      "726    1\n",
      "727    1\n",
      "728    1\n",
      "729    1\n",
      "730    1\n",
      "Name: season, Length: 731, dtype: object\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "0       1\n",
      "1       1\n",
      "2       1\n",
      "3       1\n",
      "4       1\n",
      "5       1\n",
      "6       1\n",
      "7       1\n",
      "8       1\n",
      "9       1\n",
      "10      1\n",
      "11      1\n",
      "12      1\n",
      "13      1\n",
      "14      1\n",
      "15      1\n",
      "16      1\n",
      "17      1\n",
      "18      1\n",
      "19      1\n",
      "20      1\n",
      "21      1\n",
      "22      1\n",
      "23      1\n",
      "24      1\n",
      "25      1\n",
      "26      1\n",
      "27      1\n",
      "28      1\n",
      "29      1\n",
      "       ..\n",
      "701    12\n",
      "702    12\n",
      "703    12\n",
      "704    12\n",
      "705    12\n",
      "706    12\n",
      "707    12\n",
      "708    12\n",
      "709    12\n",
      "710    12\n",
      "711    12\n",
      "712    12\n",
      "713    12\n",
      "714    12\n",
      "715    12\n",
      "716    12\n",
      "717    12\n",
      "718    12\n",
      "719    12\n",
      "720    12\n",
      "721    12\n",
      "722    12\n",
      "723    12\n",
      "724    12\n",
      "725    12\n",
      "726    12\n",
      "727    12\n",
      "728    12\n",
      "729    12\n",
      "730    12\n",
      "Name: mnth, Length: 731, dtype: object\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "0      2\n",
      "1      2\n",
      "2      1\n",
      "3      1\n",
      "4      1\n",
      "5      1\n",
      "6      2\n",
      "7      2\n",
      "8      1\n",
      "9      1\n",
      "10     2\n",
      "11     1\n",
      "12     1\n",
      "13     1\n",
      "14     2\n",
      "15     1\n",
      "16     2\n",
      "17     2\n",
      "18     2\n",
      "19     2\n",
      "20     1\n",
      "21     1\n",
      "22     1\n",
      "23     1\n",
      "24     2\n",
      "25     3\n",
      "26     1\n",
      "27     2\n",
      "28     1\n",
      "29     1\n",
      "      ..\n",
      "701    2\n",
      "702    1\n",
      "703    1\n",
      "704    1\n",
      "705    1\n",
      "706    2\n",
      "707    2\n",
      "708    2\n",
      "709    2\n",
      "710    2\n",
      "711    2\n",
      "712    1\n",
      "713    1\n",
      "714    1\n",
      "715    2\n",
      "716    2\n",
      "717    1\n",
      "718    1\n",
      "719    2\n",
      "720    2\n",
      "721    1\n",
      "722    1\n",
      "723    2\n",
      "724    2\n",
      "725    3\n",
      "726    2\n",
      "727    2\n",
      "728    2\n",
      "729    1\n",
      "730    2\n",
      "Name: weathersit, Length: 731, dtype: object\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "0      6\n",
      "1      0\n",
      "2      1\n",
      "3      2\n",
      "4      3\n",
      "5      4\n",
      "6      5\n",
      "7      6\n",
      "8      0\n",
      "9      1\n",
      "10     2\n",
      "11     3\n",
      "12     4\n",
      "13     5\n",
      "14     6\n",
      "15     0\n",
      "16     1\n",
      "17     2\n",
      "18     3\n",
      "19     4\n",
      "20     5\n",
      "21     6\n",
      "22     0\n",
      "23     1\n",
      "24     2\n",
      "25     3\n",
      "26     4\n",
      "27     5\n",
      "28     6\n",
      "29     0\n",
      "      ..\n",
      "701    0\n",
      "702    1\n",
      "703    2\n",
      "704    3\n",
      "705    4\n",
      "706    5\n",
      "707    6\n",
      "708    0\n",
      "709    1\n",
      "710    2\n",
      "711    3\n",
      "712    4\n",
      "713    5\n",
      "714    6\n",
      "715    0\n",
      "716    1\n",
      "717    2\n",
      "718    3\n",
      "719    4\n",
      "720    5\n",
      "721    6\n",
      "722    0\n",
      "723    1\n",
      "724    2\n",
      "725    3\n",
      "726    4\n",
      "727    5\n",
      "728    6\n",
      "729    0\n",
      "730    1\n",
      "Name: weekday, Length: 731, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print ('\\n%s属性的不同取值和出现的次数' %col)\n",
    "    print (train[col].astype('object'))\n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">类别型特征的取值不多、类别性特征可以采用独热编码（One hot encoding）/哑编码</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\" color=\"red\"><b>2.数值特征的分布</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000020A214F7B38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000020A21797FD0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000020A217C4710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000020A217EEE10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"5\" color=\"red\"><b>3.特征与目标之间的关系</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.1每年骑行量的分布</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">violinplot中用x表示类别（年）信息</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a214f3710>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr',\n",
    "                         'cnt']],\n",
    "             x=\"yr\", y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">2011年和2012年的分布差异很大</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.2一年中每天的骑行量</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">用颜色参数hue表示类别（年）信息</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'dayly distribution of counts')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                        'cnt',\n",
    "                        'yr']],\n",
    "            x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">每年开始和结束的数量少，中间多，骑行量和季节/月份有关</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.3季节与骑车数量的关系</b></font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">violinplot得到详细分布</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a223d9898>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['season',\n",
    "                         'cnt']],\n",
    "             x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">能看出来每个季节骑行量的分布不同，barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。谨记barplot展示的是各种变量分布的平均值</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['season',\n",
    "                      'cnt']],\n",
    "          x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">骑行量和季节的关系就很明显了：第2、3、4季度的骑行量明显高于第1季度</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.4月份与骑车数量的关系</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "sn.barplot(data=train[['mnth',\n",
    "                      'cnt']],\n",
    "          x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.5天气和骑车数目的关系</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax =plt.subplots()\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                      'cnt']],\n",
    "          x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.6工作日和节假日的分布</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a2269f320>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(ncols=2)\n",
    "sn.barplot(data=train,x='holiday',y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\"><b>3.7数值型特征与y之间的相关性</b></font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20a22736d68>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gr7fvS78RvTMshjGJ+XsP3FeB1FPvCPQHKuPcxPVrEUnNNIXvAh8A1YBoYClQHGgADAPeE5HS6Xi8hIaGcPJkePzzmOgYsmXLFv988aLl/Hv8xEXbxcTEMGP2V7Tv8CQL5i1Jl9jR0dEJYi9ZtOKi2L3e6ML8eYvZvGlbmmL6avHcpUSdj8rQGMZcxGrqfud9VZ2rqn8Dg4HCwHWp2H6yqs5Q1W3AZCAE6Og+H4zTWy+fngccHh5BSEhw/POgoCCifbxP4gP3PkWLpq35cuLHmRb7kcda0vbJR/hhzgSuv+E6vpsxLk2xjfFLMal4+LFASuo7vH6P62LmTsX2271+PwUcUtXTAKoaDZwHcl3WESby28p1NG7aCIBbalVjy2ZNcZuXX32RRx+/3znIyFNEx6TtW3C/rVpL47sbAlCzVlW2bPkrxW1urdaE+1s8yf0tnuSfw0d45IF2aYptjD8KlAulgVRTTyq7Xar8ktTrPp/oeYafj2fPmk+jO+vz089T8Hg8vNTxdTq91I6dO/fw09xfktxm0lffMfqTwbR9qhXZsgXRpePrSbZLyZxZC2h0R33mLpiMx+OhS8c36Ni5Hbt27uGnH5OObUwgi/XzC6C+CqSknpxzQD6v5yWz6kC8xcbG8urLfRMs+/uvnRe1q1bpwh2Ejhw5RquHnkuX2D2690uwbPvfF8euUfnOJLe/1PL0cmj/Ydrf1zlDYxiTgJ+XVXx1tST1NcCrIrIO+A/QI4uPxxjjZwLkHhkBVVNPzktAFLAeZ4RL2moWxpjAFSAXSj2BMomNPysYWiZL3uSsvvF0VrIbT191Lvt/9qPNGvr8d1roxyVZ98eVgqul/GKMMcnz8x64ryypG2MMEBMg33ezpG6MMQTOhVJL6sYYAxCbeWVyERkEtFHV4pdYXw9YnmhxpKqGpLRvS+rGGEPm9dRFpCbQEziQTLMKwJ/A3V7LfDpCS+rGGAPExmR8T11EcgLjgRVAsWSaVgQ2q+qh1Ma4WsapG2NMsmJjfH9chr4480xNTaFdRSBN06FaT90YY4CYaN976iJy8ZzYiahq/kTb1ABewLnnwyMpbF4RiBCRDcC1wBLgVV967tZTN8YYnPKLr4/U8iq79EwpMYtIPpypw3MAzwNtgRLATyKSI6VY1lPPBBHnTmf1IWS6NWdSnso3o9S6rix1i9yRcsMMYt9mvTKl5sv1iXvhPugDHFDVL33Yd5ib2CPdab8RkQeBg0BD4OfktrekbowxZPiF0rbAf0Qkwn2eA8jhPm+mqsu8G6vqyUTPD4vIMaBoSoEsqRtjDBme1BvhJPI4bXFKK41INLRRROoAC4CKqrrXXXYzUAjYmlIgS+rGGEPqLpSmlqru8X4uIkeBKFXd7j4v6LY7DvyBU2oZKyKvANcAI4DFqvpbSrHsQqkxxgCxsR6fHxlguvtAVc8CzYAInFEv84G/SHnEDGBT72aKnLmKXnVvckwW/n9V67qyWRYb7EJpFrnsTLu9QlOf/6ctvWWeTb1rjDH+LCYT537JSJbUjTEGMqqskuksqRtjDJkz90tmsKRujDFk7OiXzJQuo19EZLyIfHcZ238nIuPT41jSm4i8JCK7s/o4jDEZKybW4/PDn6XXkMZuOAPpTSq1aNGYFctns3TJDzz7bJtLthsypB/t27eNf9606R0sWzqTZUtnMmLEu5kau3v3F1m1ci4rls/m/pb3pCn2vS2asHLFHH5dOpPnkohdtWpFFv8ynYULpjJ39iSuv74QAMOHDeS3VT+ycMFUFi6YSt68oWmKn5wK1cszaurwdN+v8W9ZPKQx3aRL+UVVw9JjP1eb7NmzM2RIf+rVa0Fk5CmWLP6eOXMWcPjwkfg2hQoVZOzYjyhTpgTDhu0AICQkmPf++yaNm7Ti2LF/efXVjhQqVJCjR49neOx8+fLSudOzlK/QgODgPKxZPY8fZv6U6tc9dEg/6rixly6ZwexEsYd/MIBu3fuwYcNm2j/fll49OtOj1wBqVK9M8xZtOHbs31TF9NUTHR+n2cNNOH36TIbs3/ivQBndnWxSF5H1wGRVfc99Pgp4BsinqlEiciPwf8AEII+qPiIizwAvAd8C3XG+GvsT8KKqRrr7eRLoB/wH+BrI6RUzFPgfcA/ON6mWAV1U9W8RaQTMAF4G/gvkduN0U9XT7vZlcb59dTtwBJgC9HEH9PuyvhYwEmd6zDU4k9lniPLlyrBjx25OnHDOictXrKFB/VuZNn1OfJuQkGDefnsY9zS9MEFV3bq3sGnTNga/35cSJW5m3LhvUpXQLyd2ZOQp9u7dT3BwHoKD8xCThsmly5dPGHvF8jU0aFCbadNmx7dp07YThw79A0D27Nk4c/YsHo+H0qVL8L8xg7nh+usYO+4bxn85JdXxk3Ngz0Feb9+XfiN6p+t+jf/z97KKr1Iqv/wI3On1/A6cRFrDfX43zkTuif+yqwC3AY2B9sBDQAcAEbkDGAsMc/cTAdznte07QEmcORFquPse67U+BOdWUA8DLd0Yo919XwPMA3YC1YEncU4OH/m4viDOCWiDG/srnBNThgjNG8LJsAvz9kSER5I3X94EbXbv3seaNX8kWFbo2oI0bFiP3m8O4r6WT9Kly/OUKVMiU2ID7N//f2xY/wu/rfqRUaPGXrQ+JXlDQwg7GR7/PDwignyJyihxCb1unVvo1KkdH370KcHBeRg1ehxPPd2F5vc+QYcOT1O5cvlUx0/O4rlLiTofILeVN6lytZRffgS6unMBFwCKAwtxEvZqoCkwF2cSd285gPbuvMGbReQnoKa7rgMwQ1XjEvErOIk1TnEgHNilqhEi8ry7LE42oIOqrvDa/lsR6YaT6M8DnVU1FlAR6QAsE5FePqx/DDgLvKSq54Ftbs89bYXjSxjQvyf16t1K5crlWe2VNENCgwk7cTKZLR3Hjv/L2rUb4ssVy379japVKvL337syPPY999zBjTdeT1mpB8Cc2ZNYseJ3fv99fYrbDhzQi/r1ajmxV1+IHRoSwomwi2O3atWSN17vQsv7n+Lo0eMEBQUxYuTn8aWRxYuXU6VKBf78M8U5joxJUXSADGlMqae+AicJ1sHppf8G/ALcJiIeoAkwJ4ntwhNNBH+SCzOUVQLWxa1wk+sar7b/xTkBHHFPBi1xes5xooGVXs9Xu/sWnLuFlATCRSTCndZygfs6y/iwvhLwp5vQvfefrvr1H0KTu1tR9KZqlCpVnAIF8pMjRw5ua1CbVb+tTXH7des2UqGicO21BciWLRu1b63B1q1/Z0rsf/8N4/SZM5w9e5azZ88SFhZG/vx5U9wOoG+/wdzVpBWFi1ajVKkS8bEb3FabVasSxm7T5iE6d3yGuxq3YteuvQCULVuSJYtnEBQURPbs2alf71b++ONPn2Ibk5Kroqfu1s1/xinB3IgzucwS4FWc8kRczfupRJueS2J3nkv8Htc+uxtzlYgUxynJNAMGAZ1E5Fa3bQwJyz1xJ6Zodx8rgXZJxD/gw/pLHVuGiIqKolevAcyZPZGgoCDGfzmFgwcPUb5cGTp2eoauXd9McrujR4/T5633mDN7EgDffTebzVs0U2IvX76atb9v4Ndls4iJiWH5ijX8/PPSVMfu2WsAc+dMcmKPn+zELl+GTh3b0e3lt/hw2ED27jvId99+BsDSZasYMPADvvlmOst/nUXU+SgmTPyOLVuy7mYcJrAESk09xQm9ROQ5nIuj+YGuwK/ACeAHIJeqPuyOMQ/xulA6VFULee3De/03OBdV7/davw7YqKrPiMgbwBpV/dldVxZQoDaQB1gEVFXVje76h4BJOHMNtwUGAMVV9Yy7/lagF/Ac8LgP6wcCN3tdOB0GPKSqxX1/WxOyCb0yl03odVW67Iy8qvBDPv9PW+fgdL89A/gyTv1HnIRaGljlliZW4CTAuWmI+RHQXEReEZGyIvIOUNVrfVFghIjUE5ESOCeUkzhTT8b5TERquKNhPgA+d0fWTMTpsX8pIpVEpC4wDgh2h12mtH6yu/4zESknIm2w8ffGXBWumi8fqepBnLtt/B43bBCnBOPBSfipoqqrgFY4o2I2ABWAb7ya9AKWA9+7ce8Emquq9927J+OMYvkOmAa84u47EmdETgGcWvgsnHp9Gx/Xh+GMpimGM1F9D8C+hWLMVSA61uPzw59dUfOpuz3zRUCoqkak0NxvWPklc1n55ap02Zl26Y2tfP6f9vZDU/02s9uEXsYYA8QESNfLkroxxgAxl9/Z9wtXVFJX1cWkw8csY4xJLDZAUssVldSNMSajpH4WI/9kSd0YY4Bo66kbY0zgsJ66McYEEKupG2NMAAmQSRotqRtjDNiQRpMKxfLekNWHkOn2RxzNsthrj/o2DXFGqFmoDHWL3JFywwxi32ZNu+isPoB0YkndGGOAGI/11I0xJmAEyCwBltSNMQYyZ0ij143v6+Pcn3k8zo3vL7oxrohkx5lavA1Orp4A9FDVZG/cY0ndGGPI+NEvIpID58b2v+HcsrMwTqI+B/RLYpNBOHd/awnkBL502/ZILo4vN8kwxpiAF4PH50caFcG5f8OLqvqXO5fVVJz7PycgItcAnYBXVXWlqi4BugAdRCR3ckGsp26MMUB0BvfUVXU38FjccxGpATyI0wNPrBoQDHjfAHiJu6wazr2Wk2RJ3RhjSF1NXUROpNRGVfMns/1mnLu+rQWGJtGkCBDp3o0tbn8nReQUzi0/L8nKL8YYgzP6xddHOngSaALkAaYnsT4PcDaJ5WeBXMnt2HrqxhhD6i6UJtcL93H7dQAi8gzwm4hUVNXNXk1Ok3TyzgWcSm7f1lP3gYjsFpGXsvo4jDEZJyYVj7QQkaIi8lCixZvcn9clWr4fCBaRUK/t8+L04A8kF8eSehbyeDwMHPIG384dx8QZn3BziYSlsj6DevL9zxOZOOMTJs74hJDQkPh1tepWZ+n6OVdU7ObN7+LXX2eyePH3tGv3+EXrS5YsxsKF3/Hzz1P56KN38Hg8NGnSkHnzJjNv3mTmz59CRMRORErz1Vcj45dv2/YrX3010ufjaNGiMSuWz2bpkh949tk2l2w3ZEg/2rdvC0DVKhVYMH9q/ONk2HbuvrtRqt+DlFSoXp5RU4en+35NyjI6qQMCfCci3n9stXAqOlsTtd0ARAK3eS1r6C7bkFwQK79koSbNG5Hrmlw82rwd1WpW4o0B3en41Kvx6ytUKcezj77Ev8cTXpO5sfANPNuxLdlzpP2fL7NjZ8+encGD+9KgwX1ERp5m0aJpzJ27kMOHj8S3ef/9PvTvP5Rly1YxYsS73Hff3cycOY8FC5YA0L37i6xc+Tuq23nqqS4A5M+fl59+mkyvXm/7fBxDhvSnXr0WREaeYsni75kzZ0GC4yhUqCBjx35EmTIlGDZsBwAbNm6hyd2tAHj4oRb83/8dZv78xal6D1LyRMfHafZwE06fPpOu+zW+yejRLzgjWTYAX4lIV5ze+afAp6p6WEQKAqjqcVU9LSKfAR+LyNM4t/EcCYxS1WT/B/G7nrqIFBORGSJyUkQOi8hHIpJDRMqIyEwROSEiZ0Vkk4i09NruQXfZGRHZJSI9vdYlKJ+ISHERiRWRSu7zG0RkkogcEZFzIrJDRJ7P6Ndas3Y1li5cAcD6tZuoVK1C/DqPx0PxEjfxzgdvMnnOFzzSxnmpOXPl5O2hvenf670rKna5cqXZsWM3J06c5Pz586xYsYb69WslaFOjRmWWLVsFwPz5i7njjgbx64oUuZHWrR/k3Xc/SrBNnz6vMGbMlxw69I9Px1G+XBn3OMI4f/48y1esoUH9WxO0CQkJ5u23h/H1pIuvX+XJk5u+fV+l+yt9fIqXGgf2HOT19n3Tfb/GNxndU1fV88B9wL/Ar8C3wBygm9tkOgkvmr4OzAdmuct/AN5MKY5f9dRFJBewANgF3I4zJnMSztdpH8YZ/lMH56zVFxgnIv8BCgBTgI7Azzgfab4WkXWqutCH0F/hnODuwrkI0RkYIyKzVfVQ+r3ChEJCQwgPj4h/HhMdQ7Zs2YiOjiZPntxM+GIKY8dMIltQEBNmfMKf67fwVPvWfD5qAocPHUlmz/4XO2/eEE6eDI9/Hh4eSd68eRNd3ZJaAAAgAElEQVS08XhNqBQeHkm+fPHlRLp2bc/IkV9w7tyFb0hfd921NGpUn549B/p8HKF5QzgZdjL+eUR4JHnzJTyO3bv3sXv3Pu5pevFsi+3atWbatDkcO/avzzF9tXjuUm4sevXN6OkvMmPuF1Xdj5PLklrXKNHzs0AH9+Ezf+upNwaKAW1Vdb2qLgdeBA4CnwNdVHWbqm7FGdtZELgB5+u2OYB9qrpHVb/DSdCbkgqShDk43/LaqKrbgXdwTnhl0vG1XSQiPILgkOD450FBHqKjnQlAT58+w5effMOZ02eIjDzFqmVrqFC5HLfUqUaXnu2ZOOMT8uXPx/BPB/l17H79ejBv3mSmTv2C0NALSTo0NJgwr+QKEBMTk+R6j8dDs2Z3MnXqzATtH3ywOVOm/JBgu0sZ0L8nC+ZPZfq0cYTmvXAcIaHBhJ04mcyWCbV+/EHGjvva5/bmyhHj8f3hz/wtqVcA9qhqfFdQVeep6ihgDPCwiHwiIouAX9wm2YD1OD36eW7pZARwWlUP+xh3DFBHRD4WkfnANq99Z5h1qzfQqHF9AKrVrIRu3R6/rkSpm/lm9hcEBQWRPXt2atauxp/rt9C07sO0feBF2j7wImEnwuj+Qm+/jj1gwFCaNn2cYsVqUqpUMQoUyEeOHDmoX782v/22NkHb9es3c9ttdQC4++5GLF++GoCKFQXVHZw5k3DY7p13NvC5rt2v/xCa3N2KojdVo1Sp4hQokJ8cOXJwW4ParEp0HJeSN28ouXLlZP/+//OpvbmyZMKF0kzhV+UXnMlqLvoUJCLBOJPgnAa+B2bilGQWA6hqLNBWRIYC9wPNgU4i0k5VJySxz/jXLSIeYB5Ob38ysBDYDGh6vrCkzJ+ziPoNazNlzlg8Hg+vdx1Auw5PsGfXPn6Zt5RZ035k6k/jiTofxYxv57Bdd16xsaOionjttbeZNWsCHk8QX331LQcPHqZcuTJ06PA0L7/8Fq+//g6jR79Hzpw52bZtO9OnzwWgbNmS7N6996J9lilTkl27Ll6e0nH06jWAObMnEhQUxPgvp3Dw4CHKlytDx07P0LXrpUuWZcqUZM+efal74eaKESg3yfDExvrPLMIi0gwnaRdW1ePusqdwJrKpAuSLu/IrIo/i1NFLAnmBp1X1Fa99fQ3kV9XmIqLAV6r6rrvubpxEXhkn4W8CSqk6mUtEbsU5idypqotEZDcwVFU/TsvrKnNdTf95kzNJVt75KDom6/48axbK0Ipdiq7iOx9ddlFkcLG2Pv+d9toz0W+LMP7WU58PbAfGi0hvID/QH/gLZ+rJx0RkMVAD+NDdJhdwHOgoIv8CE3F63XVwprUEd2Y0EZnr7ucdLvTeT+CcpB8XkUlA3HzHcfs2xlwF/L2s4iu/qqmrajRO+SQ7sBpnWspvgXtxhvK8B2zBGfnSA2doUE1V3Qc8hDPj2WZgGjADeNfd9Zs4J4aVOCNd+uP+G6rqAeAF97ENJ6GPBjbizHlsjLkKZPLcLxnGr8ovgcrKL5nLyi9Xpcsuh7xb7Amf/07f3DPJyi/GGOPPAqX8YkndGGMInNEvltSNMQb//1KRryypG2MMEOP3l0B9Y0ndGGPw/1EtvrKkbowx2IVSY4wJKFZ+McaYAGKjX4wxJoBYT92YZGTlN5VjsjD2gdNZ903aIrkLUbfIxTf2yCxX+rdZAyOlW1I3xhjALpQaY0xAiQ2QvroldWOMAaIsqRtjTOAIjJRuSd0YYwAb/WKMMQHFLpQaY0wAsQulxhgTQKynbowxASTaeurGGBM4svKbyOnJL5K6iDQCFgGhqhqRQtviQFVV/SETDi0u5ktAD1UtnlkxjTGZKzNTuoh4gLnAHFX9+BJtCgMHklh1napecj6KoPQ5xMu2AvgPEOlD23HAbRl7OJnD4/EwcMgbfDt3HBNnfMLNJYomWN9nUE++/3kiE2d8wsQZnxASGhK/rlbd6ixdP+eKit28eWN+/XUWixd/z7PPtr5ofcmSxfjll2ksXPgdI0a8i8fj3F9s6ND+LF8+m/nzp1CrVrUE2wwe3Jfnn2+bquO4t0UTVq6Yw69LZ/Lcs20uWl+1akUW/zKdhQumMnf2JK6/vhAA3bq2Z8Wvs1jx6yz6vNU9VTET83g8DPqgD9/Pm8iUmWMpVuKmJNt8+e0Y2j7T6rJi+apC9fKMmjo8U2L5oxhifX5cDhEJAkYC96TQtAJwAic3ej+OJbeRX/TUVfUccMjH5gFyJ0Fo0rwRua7JxaPN21GtZiXeGNCdjk+9Gr++QpVyPPvoS/x7/ESC7W4sfAPPdmxL9hxp/+fL7NjZs2dnyJC+1K9/H5GRp1i0aDpz5vzM4cNH4tsMHtyX/v2HsHTpKkaOHMR9993N+fNRlC1bkgYN7qNgwfzMnDmB+vXvpVChgnzxxXDKlCnJsGGfpOo4hg7pR516LYiMPMXSJTOYPWdBguMY/sEAunXvw4YNm2n/fFt69ejMqDHjaN36QerVv5fY2FiWLPqeGT/8xJ9/bk3V+xCnaYs7yZUrFw82bUv1W6rQ5+2ePN+2a4I2Pd/sQv4CedO0/9R6ouPjNHu4CadPn8mUeP4oM0a/iEhJ4EvgJpyEnZyKwDZV9TU3AmlM6m4JZBfQB+gO/AYMAIYBNYF9wGfAUFWNcbe5010vwO/AL0BDVW2UuPwiIi8AvXBe+C5gkKp+JSLjgYZAQxF5RFWLi0go8AHwCM4nqF+Abqp60I0bC7wDvIBz4qgBlAJGALcDR4ApQB9VPetuUwvnTFoFWIPzSSLd1axdjaULnV2vX7uJStUqxK/zeDwUL3ET73zwJtdeX5DvJv3Ad1/PJGeunLw9tDdvvfIO3y+ceMXELleuNDt27ObEiTAAVqxYQ/36tzJ9+oUef/XqlVm6dBUA8+YtonHj29m79wALFiwhNjaWY8f+JTo6mhtuuI7cua/hnXeG07Rp6mYlLF++TMLjWL6GBg1qM23a7Pg2bdp24tChfwDInj0bZ86eZd++g7S49wliYpwxEjlyZOfMmbOpiu2tVp0aLP7lVwD++H0jVbzef4DmLZsQExPL4p9/TXOM1Diw5yCvt+9LvxG9MyWeP8qk0S91gD+B+4F1KbStCGxLbYDL7anfC9TF+UgwDye5PgOUBv6HU955T0RKAHOA4cBjOB87hgLLE+9QRGrgJNRWwAagBTBeRFYC3YCywHqgn7vJp0BhoClwGugLzBOR6qoa5bZ5ArgTyOE+5gE/uvu7AfgYyAt0EJGCwE/Ad+5rqQ+MwvdPEj4LCQ0hPPzCJYSY6BiyZctGdHQ0efLkZsIXUxg7ZhLZgoKYMOMT/ly/hafat+bzURM4fOhIMnv2v9h584YSFhYe/zwiIoJ8+UITtIkrtzjrI8mXL5SNGzfTrdsLjBnzJTfdVJgKFcoSHJyHnTv3sHv3vlQn9byhIYSdvHAc4RER5Mub8DjiEnrdOrfQqVM77rjzIaKiojh27F8ABr/Xhz/Wb+bvv3emKra3kNBgwk9eeP+jYy68/2XLl+b+h5vT4ZlXeLlXhzTHSI3Fc5dyY9EbMiWWv4pORVoXkZR62ahq/iSWfQ187e4jpV1UBCJF5DfgZpzOcHdV3Z7cRpeb1D9U1b9E5Algjaq+7S7/W0TewOkNvwe0B7aqalw3QEWkLnBjEvsshnPS3KOqe4DRIvI3cERVw0TkHHBKVY+4H2UeB4qq6gEAEXkSOIpz4ojrfn2qqpvd9e2A80BnVY11j6UDsExEeuGcdM4CL6nqeWCb23NPqf6VahHhEQSHBMc/DwryEB3t3H/l9OkzfPnJN5xxPw6vWraGCpXLcUudahQrUZQuPduTL38+hn86iO4vpL53lVmx+/fvQb16tahUqTxr1vwRvzwkJIQTJ04maBvXC3bWB3PixEl+/nkZNWtWZd68yWzcuIU//vgzPrmmxsABvahfrxaVK5dn9eoLxxEaEsKJsJMXtW/VqiVvvN6Flvc/xdGjxwHIlSsXn3/2AeHhkbzU5Y1UH4O3iPBIQhK8/0Hx7/8jj93HjYWvZ/IPX1D05sKcP3eeffsOsmThRX0gk478cJx6eUCBV4EooDewREQqquolTyqXm9R3uD8rAo1ExHvkShCQW0Su5UIZw9tK4MEk9vkTTg9+vYhsxknM4y7xIuI+s2qis14enDJPXFLf4bWuIlASCPfaxuMebxmgEvCnm9DjrCYDkvq61Ru4s+nt/PjDAqrVrIRuvXACLlHqZoZ/+l8euOsJgoKCqFm7GtOnzKZp3Yfj26zYPC9NCT0zY/fvPxRwatnr1y+kQIF8REScokGD2nz4YcJa+IYNm7n99josXbqKpk3vYMmSlZQuXYIjR45x112PULTof/jii+GEJZGEU9K33+D44/hzw2IKFMhPREQkDW6rzQfD/5egbZs2D/HC8225q3Er/v33wv92308by6LFyxkydHSq4yf2+29/0LhpI2bPmEf1W6qwbcvf8esG9b9wsbL7ax05cvioJfRMkJobuyTVC88ARYFor7LwY8BenFLz55fa6HKT+mmv/UwD3kqiTRhOz9inkTaqelpEmgD1gPtwSjzdROReVV2YqHl2d9/VuXhE0vEkjjNum5VAuyTCxw0fSnwx9pwvx55a8+cson7D2kyZMxaPx8PrXQfQrsMT7Nm1j1/mLWXWtB+Z+tN4os5HMePbOWzXtH/cz+rYUVFR9Or1NrNmTSQoKIgvv5zCwYOHKVeuDB07Pk23bm/x2mtvM2bM++TIkQPV7UyfPoccOXLQpElDnn76Mc6ePUu3bn0u+zh69hrA3DmTCAoKYvz4yRw8eIjy5cvQqWM7ur38Fh8OG8jefQf57tvPAFi6bBXr3RNOrlw5ucct+bz51nus+m1tmo7jp9kLua1RXab/NAGPx0OPl/rwfKen2LNzLwt+WnxZr9Gkjb9N6KWqpxI9Py0iu3CS/SV50nLbMa8LpZVVdZOI/BdopqrVvNrch1MXfwbnImoLVa3htX4iTtkkwYVS4Baggaq+49V2ObBBVTuJyCJgrar2EKervQ2orqrr3bbBwETgfVVd5V4ovU9VZ7vrX3SPp7iqnnGX3YpzYfY5nHLOQOBmrzPkMOChtI5TL3NdTf/6vyUT7Au/vJr/5YiKybpbCBcOKZhlsYvkLpRlsSHLb2d32aPi7rv5Xp//TmftnX3Z8URkN85gkovGqYtIEWALcK+qLnOXheIMQnlRVadcar/pNU59FFBGREaIozHwCRDpjn75H1BeRN4VkbJuDfsxkh7vfwroJyLtRaSYiDTFqS3FlW8igNIiUkRVFZgJfCUit4lIOeArnCvMl7pqPBHnxuFfikglt7Y/DghW1TBgsrv+MxEpJyJtgOcv8/0xxvi5aGJ8fmQEEcknItcBuNcI1wEjRKS2iFQBvsEZsDE9uf2kS1JX1f04o09uwRmxMgFnmODLXgd4P/AAznCe1m6bi8oaqroap8f8Cs5Fgs+AD1R1nNtkDM6IlA3uIP6nca4Kz8BJ/PmAJpe6kKCqkcDdQAGcWvksd7s27vowoDHOBds/gB44o3aMMQEsNjbW50cG+YiE1x4fBTbi5KiVOJ3Npomu910kTeWX1BKRSkAeN2HHLRsF5FbVZzP8ALKYlV8yl5VfssaVXn5pelMzn/9O5+370W+/BJlZ3ygtAUwSkdbAJqAW8BTOVVxjjMlyNp96KqjqLBEZhFN7vxHnIutLqjovM+IbY0xK/G30S1pl2twvqvoezheRjDHG72RGKToz+MWEXsYYk9UyalRLZrOkbowx2E0yjDEmoARGSrekbowxgF0oNcaYgGJJ3fgsuydbVh+CySShOfJkWeysHL3h8XioWyR1c9unp/T44lN0rF0oNcaYgGFfPjLGmABi49SNMSaAWE3dGGMCiPXUjTEmgFhP3RhjAoiNfjHGmABio1+MMSaA2NwvxhgTQKynbowxASRQeurpcuPpQCYixUXk/qw+DmNMxopNxX/+zHrqKRsHrAV+SO8dezwe+g5+jXIVy3Du7Dn6vPIue3ftj19/25116dzjeQC2/KkMfG0wufNcw5Axb5OvQD5OnzrNa5378e+xE1dU7MSaN29M797diIqK4quvvmXs2G+SbDd4cF/++msnn38+8bLi3duiCW+++TLRUdGMGz+ZL8Z+nWB91aoV+Wj420RHR3P27DmeebYb//xzlI4dnuappx6F2FjeefdD5sz9OVVxPR4Pfd7vhbjveb9XBrF394X3vMGddenkvudbN27j7deH8HyXp6h/Rx0A8uYLpdD119KwcvNUv2aPx0PP/3anTIVSnD93nkE9hrB/94H49U92bk2TB+4iMjySiaMns/znleTNH8q3v05k57ZdACz+cRnffjEt1bF9UaF6eTr3foHOrbpnyP59ESijX6ynnrIMu2t44+YNyZUrJ62bP8ewd0bRq3+3+HV5gvPQs19XOrR9hcebP8eBff9HgWvz06rtA2zZuI0nW77A3O/n06H7s1dcbG/Zs2dnyJC+3HtvW5o0eZRnn23DDTdcl6BNoUIF+eGHL7n33ibpEm/okH40a96GO+56mOeff+KieMM/GEC37n24q0krvp/xI716dObaawvQocPT3Hb7/TRp+hgfj/xvqmPf1cx5z59o8TzD3x1NzwEJ3/Me/brQqe0rtPF6zz8f+RXtHupEu4c6cej//qF3lwFpet0N72lArlw5ad+yM6MGfUrXfh3j15UqV4K7H7iL5+/tRLfWPXmhZzty5c6FVC7LghkL6fTIy3R65OUMS+hPdHyc3kN6kPOanBmyf1/Fxsb4/PBnV01PXUSKAR8BdwKngclAD+BvYDjwIHArsB14071Z9nigIdBQRB5R1eLpeUw1alfj119WArBh7SYqVSsfv676rVX4a+t2XhvwMkWLFeG7ST/w77ETfPXpZIKCnHNx4aI3cuzI8Ssutrdy5UqzY8duTpwIA2DFijXUr38r06fPiW8TEhLMO+8Mp2nTy58FsHz5MgnjLV9Dgwa1mTZtdnybNm07cejQPwBkz56NM2fPcuzYv9So2YTo6GhuvPEmToSFpTp2jdpV+XXRKgA2rt1Exarl4tdVr1WZv7fuoFf/bhQtVoRp7nsep3HzRpw8Ec7yxb+l6XVXvbUyKxevBmDzui2UqyLx64qXKca6les5d/YcAPt27ad0+VKUq1IWqVyG0dM+5N+jJxjWZwTH/rn8f/PEDuw5yOvt+9JvRO9033dq2JePriAikgtYAOwCbgeCgUlAuNtkIPAi0Al4GxgnIoWBbkBZYD3QL72PKyQkmPCTEfHPo6NjyJYtG9HR0RQomI9bG9zCQ3e25VTkKSbM/JQNa/5k9869xMTEMG7aaMqWL8Vzj750xcX2ljdvKGFh4fHPIyIiyJcvNEGb3bv3sXv3vnRJ6nlDQwg7eSFeeEQE+fImjBeX0OvWuYVOndpxx50PARAdHU2njs/Qr28PPh71RapjB4cmfM9jvN7z/Nfm59b6NXn4zracijzNVzM/Yf3vf7Jn5z4A2nd9mp4d+qQ6pnfsSO/YMRdi79i6k6e6PEGe4Nxkz5mDyrdUYsbE2ezevpdtG/9izbK1NH2wMa++043eL6T7nwGL5y7lxqI3pPt+UytQpgm4WsovjYFiQFtVXa+qy3GSeFxR8RtVnayqW4ABwLVAcVUNA84Bp1T1SHofVEREJMEhwfHPg4I8REdHA3DieBib/tjC0X+OcSryNL+v/INylcrGt233cCfatnyBj8a+f8XFBujfvwfz50/hu+++IG/ekPjlISEhnDhxMs37vZSBA3qxcMFUvp8+jryhF+KFhoRwIuzieK1atWTUqP/S8v6nOHr0Qu909JjxFL25Orc1qEOjhvVSdQyR4ZEEh1yYb90TFBT/nocdD2PT+i0cPXKcU6dO8/uq9fHveamyJTh5MjxB/T21IsMjyeMVO8hzIfbu7Xv5btz3DJ80mG59O7F53VZOHA9j7a/rWLv8DwAW/7SMspVKpzn+lSCGWJ8f/uxqSeoVgD3eiVlV56nqGPfpX15t4/7Cc2T0Qa1bvYHbGzuJoWrNSvy1dUf8us0bt1GmXCnyF8xHtmzZqFqzEjv+2kn7rk/TslUzAE6fOk1MdNrqe1kZG6B//6Hcffdj3HxzDUqVKk6BAvnIkSMHDRrU5rff1qZ5v5fSt99g7mrSisJFq1GqVAkKFMjvxLutNqtWJYzXps1DdO74DHc1bsWuXXsBKFu2FFO//QyA8+fPc/bsWWJiUvf6/1i9kdvvct7zKjUr8ffW7fHrNm/cRukE73lFdvzlXKCsc3stfl24Ms2vHWDjmk3Uu9O54FqxRgV2bNsZvy5/wXzkL5iPFx/owrC+I7mh8HXs3LaL3h/05I4WtwNQq0ENtm38K8l9B4romBifH/7sqii/4PS2kzu9nktiWYZdII3z85zF1GtYm6/nfI7H46F314E83aENe3ftY9G8ZQx/dxSfTxkBwE8zF/L3tp0cP3aC/47sx8NtWhKULRu9uw284mJ7i4qKolevt5k1ayJBQUF8+eUUDh48TLlyZejY8Wm6dXvrsmMkjtez1wDmzplEUFAQ48dP5uDBQ5QvX4ZOHdvR7eW3+HDYQPbuO8h3bhJfumwVAwZ+wMaNW1i+bBaxsbH8NO8Xli5blarYP89dTN2GtzJx9md4PB7e6vY2T7/Ymr2797No3jI+fHc0n0523vN5M39mu5t4S5Qqxoqlaaulx1n84zJq3X4Ln878GA8e3nnlfVq/0Ir9uw+wbP4KCt/8H8bO/R9R588z8u3/ERMTw6h3P+WtYa/x8NMPcPrUaQb1GHJZx+Dv/H2ooq88gVJHSo6INAO+Bwqr6nF32VPAS8D1wFBV/dhdXhyn9l5ZVTeJyCJgrar2SGv88tffGvhvciK7Th7KsthRMdFZFlsKFM2y2KHZcmdZbI8nw/tAyVp5YNFlH8AN+cr5/Hd6OGxb1r7gZFwtPfX5OKNaxotIbyA/0B/4CngmhW0jgNIiUkRVD6TQ1hhzhcqMWrmIZAc+ANrg5N8JQA9VTapagIi8CXQGQnG+K/OSqib75ZCroqauqtHA/Thv4mpgKvAt8I4Pm48B6gMbROSqeL+MuRrFxsb6/LgMg4BmQEvgAffnoKQaikgnoCvQDrgD59rg5ykFuCrKL1nNyi+Zy8ovmS8Qyi8FQ8v4/Hd6PPzvVMcTkWuAo0BrVZ3lLrsP+Aa4TlVPJ2q/A/hQVUe6zysDG4Fiqrr3UnGs52mMMTjTBPj6SKNqON+RWeq1bIm7rJp3QxG5ESjp3VZV/wT+xakcXNLVUlM3xphkpaZqISIpTnqkqvkTLSoCRLrff4lrc1JETgGJP+IVcX8mvo73f0m0TcCSujHGkClT7+YBziax/CyQK4m2cetSapuAJXVjjCF149ST6IX74jRJJ+RcwKkk2satC0+hbQJWUzfGGJyeuq+PNNoPBItI/GRDIpIXp1eeuMwSNyfEfxIt/08SbROwpG6MMWTKkMYNQCRwm9eyhu6yDd4NVfUQsNO7rTv6JT+Q7JwRVn4xxhggJoPnSVfV0yLyGfCxiDyNMxXJSGCUqp4RkXxATq85qkYAA0VkD3AI+AyYmtxwRrCkbowxQKZNvfs6kBuYBUThTAH+prvuI6ARUNx9PgIoBIzHmWBwNs704MmyLx8ZY0wAsZq6McYEEEvqxhgTQCypG2NMALGkbowxAcSSujHGBBBL6sYYE0AsqRtjTACxpG6MMQHEkroxxgQQS+rGGBNALKkbY0wAsaRujDEBxJK6McYEEEvqxhgTQGw+dT8jIvmBx4BKQDTOHVG+VdXIQI8vIiWB8iRxH0dVnZ7R8U3mEZGdQC1VPZZoeWFgvapenzVHduWz+dT9iIhUB+bjTJ6/EeeTVBXgHHCXqv4VqPFFpCfwHhALnE+0OlZV81y8VZpj7XLjpEhVS6ZXXK/4Kd7owCv+6HSOnWWvXURaAg3cpz2A0Vx8E+XSQANL6mlnPXX/Mgrn7iYvquo5ABG5BvjUXdckgOP3xLkDzGBVzdj7isFQr9+LAN1x7i6zGucEVhN4DhiWQfF7+tguFifxpaesfO0bgJdxbuMGUN2NGScWiACezoDYVw3rqfsRETkFVFdVTbRcgLWqGhKo8UXkCFA/oz+NJBF3EfCVqo5LtLwN8Iqq3pKZx5OZsvK1i8g4oJuqnsyoGFcr66n7l/U49yjURMtrAFsDPP4o4HUR6RD3KSGT3Aq8mMTytTj1/QwnItcDZYBs7iIPznWFmqr63wwMnWWvXVXbiUh2ESmNc/9NT6L1WzIyfiCzpO5fpgJDRaQm8CtObbsGzh/eF9612PSutfpB/KnAUqCNiBwCEpRgMqK27doIvCwiXVQ1GkBEcgBvAGsyKGY8EXkOGIPztxjLheQW6x5bRib1LHvtItICGAdcS6KEjvPas120kfGJJXX/0hU4ilO79q5fHwXu93qeEbXWrI4/EdgNTObii2cZqSvwI9BSRP7ESTBVcUb+NM6E+G8AI4FBOJ+G6gEFcercn2dw7Kx87YNwTuIDACvBpCOrqRu/ICKngaqZXVN3YxcAHgcquos2ApMzo94rImeB8qq6U0TmAaNV9QcRaQJ8qKoVU9jF5cbPktcuIpE45aVtGRnnamQ9dT8jInmAElw8VjtWVf8I4PhrAAEyPamr6r8iMgGnrr0VyKGq4ZkUPgzI7f7+F05P+Qec6xrFMzp4Fr721UBlwJJ6OrOk7kdEpC3wP5w/8kyvM2Zx/EnAWBH5BthBorHqGXQNARHJCXwIvOAuKgu8LyK5gf9v7+yj5ayqM/5LGr5EJboEhFYoSnjED6BijK2KSagoJKiwFAwhoBSKliYIiVVLqS0Y6SIgWMFSFCVgK0JZlpJIIoFAoXxZqVAX4QEEoSYNUAkSNOHL9I/93tzJdHJLwj3vO/EeFyUAAAz4SURBVHdm/9aadee+M5d93nvDPmf2ec6zp9v+ZYm4LVwLfFnS8cAtwKmS5gNHAI+WDNzwvX8PuFDSJGIy22BzvNTfux/IpN5dnAFcRGiE1/ZZ/M8RGuWDO7xWag8B4HSijr0fcfAKYB7wTeL38EeF4g5wEnAJMJWYUI8DHiI2qf94iJ8bDpq895OAJ4EDqwcMbhSX/Hv3PJnUu4tXAufZfrjf4tvere6YFYcBM2zfImldNZY7JB1HlEGKYvsx4AMtlyZLehPwpO0VhcM3ee/jgC8Ay21fACDpdmAhMLdw7J4mk3p3cSnwceJkZV/Fr2r5G8V2KUXMDsDKDtefAobNmmBjVAm8E2MljS2s127y3ucRiqpWnfz5RKLfiub+HxjxZFLvLuYBd0qaTsj72rXak3s4/tMM7UlSqp7/r8CJwMzq+3VVrflUQqtfmp+woT6d6vt1xO9/y4Kxm7z3acAhtm8duGD7ksro67tkUt9sMql3F5cSyW0h9Wq1uyH+pLbvxwBvAGYDny0YdxawuJIQbk3ow8cRWu0DCsYdoL3sNHDfpxOr1pKcCCxq6N63If6ttbMK2K5w7J4mdepdRKXVnmD77n6M3wlJk4F5tvctGGMrQm3yJiKpLgO+XbDk82LGNAG42Hax4/qSRhGfBKYROvXa7l3SPxNllum2n6iujSUmljG2p5aM38vkSr27MDC2j+N3YgWRbIth+xlJV7ChVruxhF7xDLBr4Rh3AkfbvrhwnE7MApYAyyU9TJSbdgUeYMPTy8kmkkm9uzgDuFjSeXTWan+/V+NLOqjD5e2I8stdBeM2qlPfiLf6dsQGYum69k606cPrwvYjkt5C2FHsWY3jPuAHNVgv9zSZ1LuL71Rfz+rwWh0mR03GX9Dh2rPESdM/LRi3aZ16u7f6OuK+b6L8ZuGFwAJJfw88CKxpfbH0IqJy41xYPZJhImvqSV9TdQKaYftmSasJ/5kHJf0+cFUvd+CRNNSKeJ3tdEocgeRKvQuptMsiVo47AD+zXdvs22R8Sa+hg02B7UcKhWxUpw5QHZX/ie3HKznpNMIb5Uu2ny8V13Y2nu9BMql3EZJeSVjPfoD4GD6OKAG8XtJBtn/eq/El7Uf4v+zc9tLAsfGe1KlLmgN8Edhf0i6Ex/gVREu3V/DiW99tbvzRxN97D0J5sgdwb3YkGrnkTN1dnE3IvH6HQZ34LMIj49wej38+ocbYj2h2PfB4a/W1FLOAKZLuZVCr/TPgXYQ/SWk+BRxh+9+Ao4i2gdOr59NLBpa0E9Ht6jJiH+XVhAfPsiFOuiZdTq7Uu4spwFTbK6It6HqVwExgaY/H3x34sO2fFo6zAbbvl7QnG+rU/4H6dOo7M9hlaAowv3q+nPDiKclXgHuA8UQjFIAjiUNo51LP4atkmMmVenfxctoUCBW/RT1/qybj300k9tqx/QxR8riMSKqX1qhTf4Aovfwh8HoGjbSOpLzX+GTgtOr+gfUeO6cAEwrHTgqRK/XuYhHwV5WvOUR9d3vio/GShuNfO9zB2rTp3yc08n9DyOteaH1vKXmdpK2J0s90ogEywFpJXwdmD/TuLMhfEpPJGOLTwd2SzibKMocUjj2KKDm18xoa0q8nL51M6t3FTKJ5wC8I5cUS4uP5PcTKrcn4MwrE66RNP6fDtZIbpV8BJgKHE4qT0cQq9SziVGdJ3xlsf0/S64Cdbf+4ujwfOKf0xjhwJXC2pCOozNQk7UVMcsVth5MypE69i5B0PXAosA8benH8B7CopP9J2zgmsaEPyrV1SirrRNIq4GDbN7ddnwhcXodOXdIYYEcGJ65RxIb1vra/s9EffOlxXw58nZjQIFbnWwBXE/YBpbs+JQXIlXrDVMljQGnwXuK4+oAN7XNEnflgwrmv9FiuBw61vZSWjVFJ20uqbVKpmTW02SFU1JLQJB1MnF59dYeXVzF4ynfYsf00ME3SKcRR/THAsiaafyfDRyb15vkFMIdYnY0ijsS31nHXEUl+dongHSaV46uTla3sSQ2TSkN8HvhG1SP0Ntu/qTxJvgbMbW3eUWjz9EvEIa8vA9cBHwReS5ShivzNB6i8y99u+0FiH2Pg+s7Aj3v5NG0vk0m9YWz/J6F6QNJSYqW8qsYhtE8qJ1DjpNIFnEuofm4CXqiOzm9B/C7eQSTbAUrU9ccBH7FtSXcC29q+XNJzwF8Qm6jDhqQPAu+uvv1d4DRJ7ZNVIyqkZHjIpN5F2G5vFFFHzPZJ5RDbT7a+p/Ibf1vdY6uJDzccfw2DHabuA/YGrgF+RJzuHG7uAj7NoA3D77Gh0mVgEj+6QOykBnKjNFlPZWJ1AVGOadelr7Pd84uAyiJgL+C+Oo7KS7qK8JmZCXwEOI5YSU8DzrD92wVjfws4MS0Beos8fJS0cg7wOPAxYgV5JFFzfppIMj2HpN0l3SjpnVX9/I7q8bCkd9YwhDnA24FjgH8EtiWS/LeAr5YMbPsTwPaV5w+SJkk6T9JRJeMmZcmknrSyN3Cy7SsJGeWjts8kDK8+3ejIyvFVYDXh9zKD8L0R8HdsWE8vxUOEXnxNtRE7gWhGvRg4s2RgSTOIblfjJe1B+JrvA5wj6XMlYyflyKSetPI8sUqEqO/uUz1fSujme5H3ACfZXknU1xfavp/Qb+8z5E8OD2cRvjMPANj+FTHRiGjgUZI/B2bavg74BPCA7XdX4/lk4dhJITKpJ63cDvxJZcd6F3Bgdf0t9O6x8bXAFpK2JSSd11TXX0s9WvXDibZ5620YbF9CbFR+vHDs3Rg81Tu15fkywmc+GYH0/MZXskl8nvBgeQy4CPhspWXekVi59iKLiXtbTdgNXy1pf8I+4F9qiP8yYs+inVVEr9KS/Bewl6SxxCexgT6tBxDlqGQEkiv1ZD22f0hol+dXWvl9ifLAMdTjLd4ExwP/TqzYp1Tlj/HADdRzz0uBMyWtP1FaJdm51RhKMo+o598GLLF9a3W69GvEoahkBJKSxiRpkKrb0RLgdcDDhE58V6LG/iHbDxWO/zZgF8JbaK2k9wK/rib4ZASSST3pOyR988W+1/YxJccC67Xx7yPsGJ4lNql/YHuoxtCbG+tlA3YHrRYInajRUz4ZRrKmnvQjr2h5viVhmHYv0YHoWeL07N6Ebrw4tp8l5IQLawi3WtJOth9j0DhuY5SyO04Kkkk96Ttsf3TguaQLgXNtn9z6HklfBN5Y99hqYDLwRMvz/KjeY2RST/qdaXT2tZlPNGXuKWzf2PL8hgaHkhQik3rS76wkVqz3t12fSmxc9iyVgVunlfo6ogy1HLjMdh2tFJNhIpN60u+cBlwkaTJwJ+FeOAE4CPjoUD/YA9xInCq9Eri1uvYO4DDgEkLmeaWkE2x/u5khJptKJvWkr7F9qaSfE3r1gT6sdwP72b69uZHVwiRgju2/bb0o6WbgcNuTJN0A/DWQSX2EkEk96Xva2/f1EeOBYztcv47BBuB3EHYCyQghk3rS10jahjCv2pfBjkfrsX1YE+OqCQNHAae2XT+aymCM8P15tM5BJS+NTOpJv3MhcCiwiEGHyn5hNrBA0oGERn80MbntCXxI0nii8fXc5oaYbCp5ojTpayQ9Dhxr+6qmx9IEknYjui29FXiO8HK/wPYKSW8GdrO9YKj/RtJdZFJP+hpJK4D9bS9reixNIulVwFO2X/h/35x0NZnUk75G0meInqDH2n686fHUiaRRwGeqx6uIRtdfIGyIT7L9XIPDSzaTrKkn/c5hRKPplZJW09YMxHYvN4uYA3wKmAV8o7r2T0Tz8bXV68kII5N60u+c1/QAGuRY4JO2F1ceONi+WtIawiYhk/oIJJN60tfYnt/0GBpkF/6vPQLAI0Q5JhmBZFJP+g5JlxM19KckXcEQToU9rlP/EfAxBrscDfweTiAsE5IRSCb1pB/5FYMJ7NfAC7QdOuoTTgYWV92OtgLmSnojMA54f6MjSzabVL8kfY2k3wDLiIbbC4Gb+kXWJ2kBcCbhUvlmYpG3DDjf9vImx5ZsPpnUk75G0o7AAcTK9H3EivVaIsEvsr2yweEVRdL/ABNs/7TpsSTDRyb1JGmhasQ8m6g1Y7tnW7pJOgWYQph3PQisaX3d9j1NjCt5aWRNPelrJI0mOh+9p+WxDeFUeOMQP9oLnF59/W6H19aRPUpHJJnUk37nKWBrIolfD8wDfmj7+UZHVQ9pqduDZFJP+p35wETCKmAMUVPfUtJttp9pcmClsd3T7fr6laypJwkgaQeiE9BE4A+A3YkV+8QGh5Ukm8zopgeQJF3CE0Sj5ZXAL4lV+3aNjihJNoNcqSd9jaQ/I3Ta7yL8xJcQDTOusf3fTY4tSTaHrKkn/c404Bqiu88t/XLwKOldcqWeJEnSQ2RNPUmSpIfIpJ4kSdJDZFJPkiTpITKpJ0mS9BCZ1JMkSXqI/wXQeRn+cIo0VAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt,mask=mask,\n",
    "          vmax=8,square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"4\" color=\"black\">体感温度和温度高度相关、目标cnt与温度正相关、湿度和风速负相关</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
